Impara Plotting Price Series | Visualizing Market Trends and Indicators

Visualizing price series is a foundational skill for any trader because it allows you to quickly spot trends, reversals, and key support or resistance levels in the market. By plotting closing prices over time, you gain a clear, intuitive sense of a security's movement, making it easier to identify patterns that could inform trading decisions. A well-constructed line chart can reveal not only the general direction of a price but also periods of volatility, consolidation, or breakout—insights that are hard to extract from raw numbers alone.


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import pandas as pd
import matplotlib.pyplot as plt

# Create a sample DataFrame with dates and closing prices
data = {
    "Date": pd.date_range(start="2024-01-01", periods=10, freq="D"),
    "Close": [100, 102, 101, 105, 107, 110, 108, 111, 113, 115]
}
df = pd.DataFrame(data)
df.set_index("Date", inplace=True)

# Plot the closing price series
plt.figure(figsize=(8, 4))
plt.plot(df.index, df["Close"])
plt.xlabel("Date")
plt.ylabel("Closing Price")
plt.title("Closing Price Over Time")
plt.show()

A typical price chart includes several key elements that make it easier to interpret the underlying data. The horizontal axis (x-axis) usually represents time, while the vertical axis (y-axis) shows the price. Adding axis labels clarifies what each dimension represents, and a descriptive title summarizes the chart's content for quick reference. These elements are crucial for trading analysis because they help you quickly understand what is being plotted and ensure that anyone reviewing the chart can interpret it accurately. Enhancing readability through clear labels, appropriate scaling, and visual cues makes it easier to spot trends and make informed trading decisions.


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import pandas as pd
import matplotlib.pyplot as plt

# Reuse the previous DataFrame 'df'
plt.figure(figsize=(8, 4))
plt.plot(df.index, df["Close"], marker="o", linestyle="-", color="b", label="Close Price")
plt.xlabel("Date")
plt.ylabel("Closing Price")
plt.title("Closing Price Over Time")
plt.grid(True, linestyle="--", alpha=0.6)
plt.legend()
plt.tight_layout()
plt.show()

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Sezione 2. Capitolo 1

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              123456789101112131415161718
            
import pandas as pd
import matplotlib.pyplot as plt

# Create a sample DataFrame with dates and closing prices
data = {
    "Date": pd.date_range(start="2024-01-01", periods=10, freq="D"),
    "Close": [100, 102, 101, 105, 107, 110, 108, 111, 113, 115]
}
df = pd.DataFrame(data)
df.set_index("Date", inplace=True)

# Plot the closing price series
plt.figure(figsize=(8, 4))
plt.plot(df.index, df["Close"])
plt.xlabel("Date")
plt.ylabel("Closing Price")
plt.title("Closing Price Over Time")
plt.show()


              12345678910111213
            
import pandas as pd
import matplotlib.pyplot as plt

# Reuse the previous DataFrame 'df'
plt.figure(figsize=(8, 4))
plt.plot(df.index, df["Close"], marker="o", linestyle="-", color="b", label="Close Price")
plt.xlabel("Date")
plt.ylabel("Closing Price")
plt.title("Closing Price Over Time")
plt.grid(True, linestyle="--", alpha=0.6)
plt.legend()
plt.tight_layout()
plt.show()

Tutto è chiaro?

Grazie per i tuoi commenti!

Sezione 2. Capitolo 1

Plotting Price Series

1. Why are line charts commonly used for visualizing price series?

2. Which matplotlib function is used to add a title to a plot?

3. How can gridlines help in interpreting financial charts?

Plotting Price Series

1. Why are line charts commonly used for visualizing price series?

2. Which matplotlib function is used to add a title to a plot?

3. How can gridlines help in interpreting financial charts?